From Noise to Insight: An Academic Study of Electrical Signal Processing

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 17 | 01 | Page :
    By

    Dr V. Basil Hans,

  1. Research Professor, Department of Commerce & Management and Humanities & Social Sciences, Srinivas University, Mangalore, Karnataka, India

Abstract

Electrical signal processing is very important for turning raw, often noisy data into useful and actionable information. This article gives a simple and easy-to-understand summary of the basic ideas and methods used in electrical signal processing, such as filtering, signal representation, modulation, and spectrum analysis. The focus is on how to effectively eliminate noise and interference to improve the quality and dependability of signals. The conversation connects ideas from theory to real-world uses in control systems, biomedical instruments, audio engineering, and communication systems. We look at important methods like time-domain and frequency-domain analysis to show how signals can be understood, improved, and used in real life. Furthermore, the paper emphasizes recent progress in digital signal processing methods, such as adaptive filtering and intelligent noise cancellation strategies, which greatly enhance performance in dynamic and uncertain contexts. There is also a discussion of how modern computational tools and algorithms facilitate efficient real-time signal processing. Moreover, the increasing significance of signal processing in new technologies like IoT, wireless communication, and smart healthcare systems is highlighted, as precise data interpretation is essential in these areas. This paper seeks to furnish readers with a robust understanding of electrical signal processing by elucidating complicated concepts, emphasising its significance in contemporary technology and its function in transforming data into valuable insights.

Keywords: Digital Signal Processing (DSP), Analogue Signals, Noise Reduction, Signal Filtering, Time-Domain Analysis, Frequency-Domain Analysis, Spectral Analysis, Signal Modulation, and Signal-to-Noise Ratio (SNR)

How to cite this article:
Dr V. Basil Hans. From Noise to Insight: An Academic Study of Electrical Signal Processing. Current Trends in Signal Processing. 2026; 17(01):-.
How to cite this URL:
Dr V. Basil Hans. From Noise to Insight: An Academic Study of Electrical Signal Processing. Current Trends in Signal Processing. 2026; 17(01):-. Available from: https://journals.stmjournals.com/ctsp/article=2026/view=239660


References

[1] MacGregor R, editor. Neural modeling: electrical signal processing in the nervous system. Springer Science & Business Media; 2012 Dec 6.

[2] Vasilescu G. Electronic noise and interfering signals: principles and applications. Springer Science & Business Media; 2006 Jan 17.

[3] Tuzlukov V. Signal processing noise. CRC Press; 2018 Oct 8.

[4] Vasilescu G. Electronic noise and interfering signals: principles and applications. Springer Science & Business Media; 2006 Jan 17.

[5] Khovanov IA, McClintock PV. Unsolved problems of noise preface to the UPoN-2018 special issue of fluctuation and noise letters. Fluctuation and Noise Letters. 2019 May 29;18(2).

[6] Guo D, Perc M, Liu T, Yao D. Functional importance of noise in neuronal information processing. Europhysics Letters. 2018 Dec 1;124(5):50001.

[7] Shipton JJ. The Application of Non-Linear Partial Differential Equations for the Removal of Noise in Audio Signal Processing. University of the Witwatersrand, Johannesburg (South Africa); 2017.

[8] Basar E. Noise modulation. IEEE Wireless Communications Letters. 2023 Dec 25;13(3):844-8.

[9] Virtanen T, Vincent E, Gannot S. Time‐Frequency Processing: Spectral Properties. Audio source separation and speech enhancement. 2018 Sep 24:15-29.

[10] Scholl S. Fourier, Gabor, Morlet or Wigner: comparison of time-frequency transforms. arXiv preprint arXiv:2101.06707. 2021 Jan 17.

[11] Sörnmo L, Laguna P. Bioelectrical signal processing in cardiac and neurological applications. Academic press; 2005 Jul 21.

[12] Sanei S, Chambers JA. EEG signal processing and machine learning. John Wiley & Sons; 2021 Sep 27.

[13] Chaddad A, Wu Y, Kateb R, Bouridane A. Electroencephalography signal processing: A comprehensive review and analysis of methods and techniques. Sensors. 2023 Jul 16;23(14):6434.

[14] Li L, Rehr R, Bruns P, Gerkmann T, Röder B. A survey on probabilistic models in human perception and machines. Frontiers in Robotics and AI. 2020 Jul 7;7:85.

[15] Knuth KH. A Bayesian approach to source separation. arXiv preprint physics/0205032. 2002 May 10.

[16] Luvton P, Castillejos A, Zhao J, Chajo C. A spectrum-based image denoising method with edge feature enhancement. arXiv preprint arXiv:2403.11036. 2024 Mar 16.

[17] Allwood G, Du X, Webberley KM, Osseiran A, Marshall BJ. Advances in acoustic signal processing techniques for enhanced bowel sound analysis. IEEE reviews in biomedical engineering. 2018 Oct 11;12:240-53.

[18] Sheng H, Chen Y, Qiu T. Fractional processes and fractional-order signal processing: techniques and applications. Springer Science & Business Media; 2011 Oct 20.

[19] Mishra M. Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review. International transactions on electrical energy systems. 2019 Aug;29(8):e12008.

[20] Boashash B. Time-frequency signal analysis and processing: a comprehensive reference. Academic press; 2015 Dec 11.

[21] Munroe O, Letourneur S, Novak A. Design of an electronic circuit for loudspeaker real- time digital signal processing. arXiv preprint arXiv:2206.07516. 2022 Jun 3.

[22] Iakovidis DK, Ooi M, Kuang YC, Demidenko S, Shestakov A, Sinitsin V, Henry M, Sciacchitano A, Discetti S, Donati S, Norgia M. Roadmap on signal processing for next generation measurement systems. Measurement Science and Technology. 2022 Jan;33(1):012002.

[23] Sztraka L, Boros B, Molnár-Paál É. A COMPUTER PROGRAM FOR SIMULATING SOME SIGNAL-TO-NOISE ENHANCEMENT METHODS USED IN HIGH PERFORMANCE INSTRUMENTS. Periodica Polytechnica Chemical Engineering. 1987;31(1-2):67-81.

[24] Martinek R, Ladrova M, Sidikova M, Jaros R, Behbehani K, Kahankova R, Kawala- Sterniuk A. Advanced bioelectrical signal processing methods: Past, present and future approach—Part I: Cardiac signals. Sensors. 2021 Jul 30;21(15):5186.

[25] Rosen GL. Signal processing for biologically-inspired gradient source localization and DNA sequence analysis. Georgia Institute of Technology; 2006.

[26] Dangeti SV. Denoising techniques-a comparison. Louisiana State University and Agricultural & Mechanical College; 2003.

[27] Six J, Bressan F, Leman M. A Case for Reproducibility in MIR: Replication of ‘A Highly Robust Audio Fingerprinting System’. Transactions of the International Society for Music Information Retrieval. 2018 Sep 4;1(1).

Ahead of Print Subscription Review Article
Volume 17
01
Received 31/03/2026
Accepted 31/03/2026
Published 03/04/2026
Publication Time 3 Days
45

Login


My IP

PlumX Metrics